Automated generation of control signals for sexual stimulation devices

ABSTRACT

A system and method for automated generation of control signals for sexual stimulation devices from videos of sexual activity. The system and method use annotation data indicating comprising indications of movements in the video corresponding to sexual activity to generate control signals for compatible sexual stimulation devices. The annotation data may be generated manually or automatically. Device control signals may be generated directly from the annotation data and synchronized with a video, or may be processed through a series of machine learning algorithms to generate models of “typical” sexual activity represented in the videos, which models are then used to generate signals for the sexual stimulation device.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed in the application data sheet to the followingpatents or patent applications, the entire written description of eachof which is expressly incorporated herein by reference in its entirety:

Ser. No. 16/861,014

Ser. No. 16/214,030

Ser. No. 16/139,550

BACKGROUND Field of the Art

The present invention is in the field of computer control systems, andmore specifically the field of control systems for sexual stimulationdevices.

Discussion of the State of the Art

In the field of sexual stimulation devices, there are examples ofcontrol systems that allow for synchronization of the device with videosof sexual activity. However, existing systems are extremely limited intheir functionality. They contain only limited libraries of manuallypre-programmed synchronized stimulation routines, cannot recognize videocontent on their own, cannot automatically create their own stimulationroutines, and cannot customize the experience for the user usingbiometric data about the user.

What is needed is a system and method for automated generation ofcontrol signals for sexual stimulation devices from videos of sexualactivity.

SUMMARY

Accordingly, the inventor has conceived, and reduced to practice, asystem and method for automated generation of control signals for sexualstimulation devices from videos of sexual activity The system and methoduse annotation data indicating comprising indications of movements inthe video corresponding to sexual activity to generate control signalsfor compatible sexual stimulation devices. The annotation data may begenerated manually or automatically. Device control signals may begenerated directly from the annotation data, and synchronized with avideo, or may be processed through a series of machine learningalgorithms to generate models of “typical” sexual activity representedin the videos, which models are then used to generate signals for thesexual stimulation device.

According to a preferred embodiment, a system for automated generationof control signals for sexual stimulation devices from videos of sexualactivity is disclosed, comprising: a video analysis engine comprising afirst plurality of programming instructions stored in the memory of, andoperating on at least one processor of, a computer system, wherein thefirst plurality of programming instructions, when operating on theprocessor, causes the computer system to: receive a plurality of videosrepresenting a type of sexual activity; process each video through afirst machine learning algorithm trained to classify objects in thevideo; identify a pattern of movement of an object in each classifiedvideo; process the pattern of movement identified in each classifiedvideo through a second machine learning algorithm trained to identify aplurality of pattern states and probabilities of transferring from oneof the plurality of pattern states to another of the plurality ofpattern states; create a sequence of pattern states from the patternstates and probabilities, the sequence representing a model of typicalmovements found in the type of sexual activity represented by theplurality of videos; and based on the model, generate one or morecontrol signals for a sexual stimulation device, the control signalscorresponding to the typical movements.

According to another preferred embodiment, a method for automatedgeneration of control signals for sexual stimulation devices from videosof sexual activity is disclosed, comprising the steps of: receiving aplurality of videos representing a type of sexual activity; processingeach video through a first machine learning algorithm trained toclassify objects in the video; identifying a pattern of movement of anobject in each classified video; processing the pattern of movementidentified in each classified video through a second machine learningalgorithm trained to identify a plurality of pattern states andprobabilities of transferring from one of the plurality of patter statesto another of the plurality of pattern states; creating a sequence ofpattern states from the pattern states and probabilities, the sequencerepresenting a model of typical movements found in the type of sexualactivity represented by the plurality of videos; and generating, basedon the model, one or more control signals for a sexual stimulationdevice, the control signals corresponding to the typical movements.

According to another preferred embodiment, a method for automatedgeneration of control signals for sexual stimulation devices from videosof sexual activity is disclosed, comprising the steps of: receiving aplurality of videos representing a type of sexual activity; processingeach video through a first machine learning algorithm trained toclassify objects in the video; identifying a pattern of movement of anobject in each classified video; processing the pattern of movementidentified in each classified video through a second machine learningalgorithm trained to identify a plurality of pattern states andprobabilities of transferring from one of the plurality of patternstates to another of the plurality of pattern states; creating asequence of pattern states from the pattern states and probabilities,the sequence representing a model of typical movements found in the typeof sexual activity represented by the plurality of videos; andgenerating, based on the model, one or more control for a sexualstimulation device, the control signals corresponding to the typicalmovements.

According to an aspect of an embodiment, a biometric sensor receiver isused to: receive biometric data of a user from one or more biometricsensors; and adjust the one or more control signals based on thebiometric data prior to or during transmission to a sexual stimulationdevice.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 shows the internal workings of an exemplary sexual stimulationdevice.

FIG. 2 shows additional components of the internal workings of anexemplary sexual stimulation device.

FIG. 3 shows the external structure of an exemplary sexual stimulationdevice.

FIG. 4 shows exemplary variations of the sleeve and gripper aspects ofan exemplary sexual stimulation device.

FIG. 5 shows the internal workings of an exemplary sexual stimulationdevice.

FIG. 6 shows additional exemplary aspects of an exemplary sexualstimulation device.

FIG. 7 is a block diagram of an exemplary synchronized video controlsystem for sexual stimulation devices.

FIG. 8 is a block diagram of the video analysis engine aspect of anexemplary synchronized video control system for sexual stimulationdevices.

FIG. 9 is a block diagram of the control interface aspect of anexemplary synchronized video control system for sexual stimulationdevices.

FIG. 10 is a block diagram of the device controller aspect of anexemplary synchronized video control system for sexual stimulationdevices.

FIG. 11 is a flow diagram showing a method for an exemplary synchronizedvideo control system for sexual stimulation devices.

FIG. 12 is a flow diagram showing a method for using annotated videodata to control a sexual stimulation device.

FIG. 13 is a flow diagram showing a method for manual annotation ofvideos containing depictions of sexual activity.

FIG. 14 is a block diagram showing an exemplary system architecture forautomated annotation of videos containing depictions of sexual activity.

FIG. 15 (PRIOR ART) is a diagram describing the use of the local binarypattern (LBP) algorithm to extract the textural structure of an imagefor use in object detection.

FIG. 16 (PRIOR ART) is a diagram describing the use of a convolutionalneural network (CNN) to identify objects in an image by segmenting theobjects from the background of the image.

FIG. 17 is a diagram showing exemplary video annotation data collectionand processing to develop models of sexual activity sequences.

FIG. 18 is a flow diagram showing a method for an exemplary synchronizedvideo control system for sexual stimulation devices.

FIG. 19 is a block diagram illustrating an exemplary hardwarearchitecture of computing device.

FIG. 20 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 21 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 22 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor automated generation of control signals for sexual stimulationdevices from videos of sexual activity. The system and method useannotation data indicating comprising indications of movements in thevideo corresponding to sexual activity to generate control signals forcompatible sexual stimulation devices. The annotation data may begenerated manually or automatically. Device control signals may begenerated directly from the annotation data and synchronized with avideo, or may be processed through a series of machine learningalgorithms to generate models of “typical” sexual activity representedin the videos, which models are then used to generate signals for thesexual stimulation device.

In the held of sexual stimulation devices, there are examples of controlsystems for such devices that allow for synchronization of the devicewith videos of sexual activity. These control systems attempt toreplicate the sexual activities shown on the screen throughsynchronization of the video with some form of sexual stimulationdevice. However, existing systems cannot synchronize with any video ofsexual activity. They must be manually pre-programmed for each video. Asa result, they contain only limited libraries of video-synchronizedstimulation routines. Further, since they are manually pre-programmed,the experience is the same for every user, and cannot be customized tothe user's preferences or biometric data. As a result, such systemscannot accurately imitate the sensations shown in the video for many ormost users, and cannot customize the experience for the user usingbiometric data about the user such as differences in anatomy. There arenumerous improvements of this invention over the prior art, such asautomated real time video analysis and synchronization, modeling of“typical” or “representative” sexual activity from one or more videos,and broad customization of the user experience based on user preferencesand the user's biometric data.

This control system uses automated, real-time video analysis and machinelearning algorithms to identify components of the sexual activity in thevideo such as movement, pressure, and rhythm, as opposed to existingsystems which require manually pre-programming the controller to matchthe perceived activity in the videos. Using real-time video analysisallows access to the entirety of sexual video content available on theinternet. Any video containing sexual content could be used with thesystem, which allows the user to choose videos with very specificcontent based on the user's preferences.

Since any video containing sexual content can be used, the system can betuned to mimic the sexual activity of particular actors or actressesengaging in specific sexual activities. The machine learning algorithmsused to conduct the video analysis could be fed metadata about thevideos such as the names of the actor or actress, such that the controlsystem could learn to “perform” certain sexual activities just like acertain actor or actress does generally, or even in a particular film.Likewise, since any video can be used with this control system, andsince the synchronization with the videos can be either automated orcontrolled by the user, the opportunities for customization and sharingare unlimited. Users would have the opportunity to customize thestimulation associated with videos in myriad ways, and share thosecustomized experiences with others by sharing the control system fileassociated with that video. Further, users could create videos of theirown sexual activity, and “share” their experience with others remotelythrough this control system and an appropriate stimulation device.

Another major benefit of this control system is the broad customizationallowed based on user profiles. At the most basic level, users cansimply watch a video, and allow the system to control the device basedon the system's automated video parsing without any adjustment or input.However, the system is not limited to such usage. The system could beused with a manual form of input such as a slider bar on the screen,which allows users to map their own perception of the movement andsexual activity in the video.

The system could allow users to set up a profile containing parametersand preferences for operation of the compatible sexual stimulationdevice. For example, the user could set a parameter indicating that thedevice should speed up or slow down when certain movements in the videoare detected. Further, combining these parameters with biometric sensordata could allow the user to indicate that the device should attempt toprolong orgasm for a certain period of time. For example, the user couldset a parameter indicating that orgasm should be delayed at least 15minutes, and this parameter, combined with biometric data of breathingrate, heart rate, penile stiffness, etc., could cause the control systemto slow down or stop stimulation until the biometric data falls backwithin certain ranges, at which point the device would continuestimulation as usual. A myriad of parameters, preferences, and biometricdata ranges could be used. For example, the control system could beinstructed to delay orgasm, prevent orgasm, or hold the user at a givenlevel of excitement.

Further, metadata can be captured from the video related to the videocontent including, for example, the actor or actress in the video, thetype of sexual activity, the position or orientation of the sexualactivity, the location or scene in which the sexual activity occurs, andthe style or category of the video content (e.g., oral sex, anal sex,gay sex, fetish). Using metadata associated with the videos, the controlsystem could select or suggest videos containing very specific contentbased the user's preferences. Such metadata may already be embedded inthe videos, may be available on the internet, or may be developed byhaving users input such metadata in a growing library of such videos.Further, biometric data, for example penis length and girth for males,can be entered into the user profile, and the stimulation provided bythe control system can be automatically adjusted to provide the user acustomized, better feeling, more realistic experience based on thosedimensions. For females, the amount of vaginal secretions could bemeasured using sensors on a compatible device, and the compatibledevice's operation could be adjusted accordingly. Optionally, othertypes of biometric data such as heart rate, breathing rate, and penilestiffness could be captured by a variety of commercially availabledevices (for example, sports training monitors), or by sensors on thestimulation device, itself, and fed back to the user profile toautomatically optimize the video content and types of stimulationpreferred by the user.

The process of training the machine learning algorithms used by thecontrol system could be aided by a number of means. For example, userscould manually tag a small subset of videos with synchronizedstimulation routines, which could then be applied by the machinelearning algorithms to very large databases of videos to learn whichvideos contain that sort of sexual activity. Clustering could be used toidentify certain types of sexual activity, based on the movement andrhythm associated with them, and pressure can be extrapolated fromsmaller sets of manually tagged videos. User ratings in some portal oronline platform could help refine the outputs and extrapolationsgenerated by the machine learning algorithms.

In some embodiments, all components of the video control system may belocated on a general purpose computer. In other embodiments, somecomponents of the video control system may be located on the compatiblestimulation device as embedded computer components or systems. Forexample, a compatible stimulation device may contain an embeddedcomputer component or systems that act as the device controller, whichreceives signals from a video analysis engine and causes the compatiblestimulation device to operate in accordance with those signals. In someaspects of some embodiments, such an embedded computer component orsystem might contain programmed sequences of movements or other contentsuch that the bandwidth required to transmit signals to the device canbe reduced by sending references to the programmed sequences ofmovements.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be continuouscommunication with each other, unless expressly specified otherwise. Inaddition, devices that are in communication with each other maycommunicate directly or indirectly through one or more communicationmeans or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Conceptual Architecture

FIG. 7 is a block diagram of an exemplary synchronized video controlsystem for sexual stimulation devices 700. In a this embodiment, a videoanalysis engine 701 inputs a video of sexual activity, parses the intoat least the components of movement corresponding to the sexual activityshown in the video, and outputs signals containing the parsed videoinformation to a device controller 702. A control interface 703 allowsthe user to enter a profile containing parameters for sexual stimulationdevice operation or the user's biometric information, stores the user'sprofile information, and outputs the user's profile information to thedevice controller 702. The device controller 702 adjusts the signalsfrom the video analysis engine 701 based on the profile information fromthe control interface 703 and outputs the adjusted signals to astimulation device 704 such that they are synchronized with the activityshown in the video. In an aspect of an embodiment, the parsed videoinformation from the video analysis engine 701 is stored in a datastorage device 705 for later retrieval and use.

FIG. 8 is a block diagram 800 the video analysis engine 701 aspect of anexemplary synchronized video control system for sexual stimulationdevices. A video parser 801 receives video input 802, sends the video'smetadata to a metadata processor 803, which checks to see if themetadata for that video already exists in the data storage device 705.If the metadata already exists, it is read from the data storage device705 and sent out the control interface 703. If the metadata does notexist, it is formatted, written to the data storage device 705, and sentout to the control interface 703. Simultaneously, the video parser 801sends the video content to the motion translation processor 804, whichchecks to see if the control signal data for that video already existsin the data storage device 705. If the control signal data alreadyexists, it is read from the data storage device 705 and sent out thedevice controller 702. If the control signals do not exist, the motiontranslation processor 804 uses video processing algorithms and machinelearning algorithms to detect sexual activity and to translate themotions in the video to control signals related to movement, pressure,and rhythm, and makes adjustments to the control signals in response todata from the control interface 805. The controls signals are thenwritten to the data storage device 705 and sent out to the devicecontroller 702. In an aspect of an embodiment, the actual video contentmay also be stored in the data storage device 705.

FIG. 9 is a block diagram 900 of the control interface 703 aspect of anexemplary synchronized video control system for sexual stimulationdevices. The user can enter device parameter settings 901 to adjustoperation of a compatible device. The user can further enter biometricdata manually, or it may be obtained automatically by the biometric datainterface 902 from biometric sensor receiver 1004 disclosed in FIG. 10.The parameters and biometric data are sent to a profile generator 903,which creates a profile for the user based on the various inputs. Theprofile information is saved to the storage device 705, and is sent tothe device controller 702. The control interface may contain a manualvideo tagging interface 904, which allows the user to adjust thesensations received while viewing those videos.

FIG. 10 is a block diagram 1000 of the device controller 702 aspect ofan exemplary synchronized video control system for sexual stimulationdevices. Control signals for the video being watched are received fromthe motion translation processor 804 into the video synchronizer 1001,which adjusts the timing of the signals to correspond with the videobeing watched. Parameters and biometric data are received into theprofile interface 1002 from the profile generator 903. A control signalgenerator 1003 receives the outputs from both the video synchronizer1001 and profile interface 1002, and adjusts the synchronized controlsignals based on the parameters and biometric data, and sends out theadjusted control signal to the stimulation device 704. The devicecontroller may also contain a biometric sensor receiver 1004 that couldallow the capture of biometric data from wireless devices such asfitness trackers that monitor heart rate, blood pressure and breathingmonitors, and even sensors in the stimulation device itself. The datacaptured through the biometric sensor receiver could be used for realtime feedback to the control signal generator 1003 and for use inimproving user experiences by enhancing the user's profile or improvingthe accuracy of video selection.

FIG. 11 is a flow diagram showing a method 1100 for an exemplarysynchronized video control system for sexual stimulation devices.According to this method, video of sexual activity would be input into acomputer 1101. The computer, using machine learning algorithms, wouldparse the video into at least one component corresponding to the sexualactivity shown in the video 1102. The parsed video information could bestored for later retrieval 1103 and any video metadata could also bestored for later retrieval 1104. Signals containing the parsed videoinformation to a device controller would be output to a devicecontroller 1105. Separately, the User would be allowed to enter aprofile in a control interface containing at least parameters foradjusting compatible device operation 1106, and biometric data 1107,which would be stored 1108, and output to the device controller 1109.The signals from the parsed video would be adjusted based on the user'sprofile information 1110 and output to a compatible device, synchronizedwith the activity shown in the video such that the compatible devicewould emulate the sexual activity shown in the video 1111.

FIG. 12 is a flow diagram showing a method for using annotated videodata to control a sexual stimulation device. In a first step, videoscontaining depictions of sexual activity are annotated (or tagged) withdata regarding one or more movements shown in the videos 1201. Theannotations are associated with playback times in the video, either asmetadata incorporated into the video file or as separate files. Theannotations (or tags) may be performed manually by a person watching thevideo or automatically by the video analysis engine 701.

The annotations may be used directly to generate device control signals1205, such as real-time use wherein the device control signals aregenerated 1205 immediately or very soon after the annotations arecreated, or delayed use by storing the annotations for later use 1202and generating device control signals 1205 from the stored annotations.In this use, the annotations will typically be used to generate controlsignals for a particular video for which the annotations were made. Asingle such annotation may be used or some combination of annotationsfor the same video (e.g., averaging of multiple annotations).

Alternatively, the annotations may be processed through machine learningalgorithms to create models of movement patterns and sequences commonlyassociated with certain videos, or certain sexual activities, persons,etc. In this use, annotations from a plurality of different videos willtypically be used. The annotations are processed through a first set ofmachine learning algorithms to detect and analyze movement patternstypical of certain sexual activities 1203. This first set of machinelearning algorithms may use techniques such as clustering to grouptogether similar types of movement patterns. The movement pattern dataare then processed through a second set of machine learning algorithmsto determine sequencing information 1204 such as how long a pattern istypically held and the probabilities of changing to different patternsafter the current pattern. The sequencing information is used to createpredictive models of typical or expected sequences of movement patterns,which mimic frequently-seen depictions of sexual activity in theannotated data. The data from these models may then be used to generatedevice control signals 1205 representing movement patterns and sequencesin common sexual activities.

FIG. 13 is a (low diagram showing a method for manual annotation ofvideos containing depictions of sexual activity. In a first step, avideo is played which contains depictions of sexual activity 1301.During playback, a human viewer moves a controller to indicate therelative motion of a movement of sexual activity located on the screen.The controller may be any device that allows the viewer to input amotion associated with a movement of sexual activity in the video beingviewed by the viewer 1302. Ideally, the controller will allow the viewerto simply imitate the motion by mimicking the motions) seen in the video(e.g., moving the viewer's hand back and forth) rather than programmingin the motion(s) (e.g., by entering a number associated with themotion). The controller may be virtual (e.g., an on-screen slider bar, aon-screen virtual joystick, gestures made in front of agesture-recognition camera), or the controller may be a physical device(e.g., a physical slider, joystick, wand, mobile phone with anaccelerometer, etc.). The controller may allow for linear motions,two-dimensional motions, or three-dimensional motions, and may alsoallow for rotation or tilting. As the human viewer moves the controllerin synchronicity with the movements depicted in the video, annotationdata are created that are associated with video playback times 1303. Asa simple example, a reciprocal motion depicted in the video may beannotated as tuples, with a series of time stamps representing the videoplayback time, each associated with a value indicating the relativelocation of the linear motion in the video at that time. The annotationsmay be incorporated into the video file as metadata or stored asseparate data files. Where the annotations will be used to generatedevice control data for a particular video, the annotation willtypically be associated with the video in some manner. However, spherethe annotations are to be used as input to machine learning algorithmsfor generation of models of sexual activity, the annotations may bedisassociated with the video from which they are derived. Theannotations may then be used to generate control signals 1305, or may beprocessed through machine learning algorithms to detect patterns ofmovement and create model sequences of such patterns mimicking themovements of sexual activity associated with certain concepts (e.g.,frequently-seen movements represented in a certain type of video, orcertain sexual activities, or associated with certain actors andactresses, etc.) 1304.

FIG. 14 is a block diagram showing an exemplary system architecture firautomated annotation of videos containing depictions of sexual activity.This exemplary system architecture provides more detail regarding theoperation of the video analysis engine 701. In some embodiments, thisexemplary system architecture, or a similar one, may be incorporatedinto the video analysis engine 701 as a component, or as a component ofthe video parser 801, the metadata processor 803, or the motiontranslation processor 804. In some embodiments, this system architecturemay be distributed among, or substitute for, one or more components ofthe video analysis engine 701. In some embodiments, this systemarchitecture or it components may exist separately from, but remainaccessible to, the video analysis engine 701.

In this exemplary embodiment, a clip parser 1401 parses (i.e., breaksbreaks or segments) a video into smaller clips to reduce the scale ofthe video processing by the machine learning algorithms (i.e., reducesthe video to more easily manageable smaller clips of a larger video).Depending on the size of the video, available processing power, and themachine learning algorithm to be used, the clip parser 1401 may reducethe video to any size ranging from the entire video to frame-by-frameclips of the video. Where a video is annotated with known activities(e.g., where the video or segments of the video have been annotated withan indication of the type of activity that is contained therein), theclip parser 1401 may parse the video into clips corresponding to thelength of the known activity, as indicated by the annotations. In suchcases, the clip parser 1401 forwards the clips of known activitydirectly to an action detector 1402. Where the video contains depictionsof unknown activities, the clip parser will parse the video into uniformsizes (e.g., frame-by-frame, or a certain number of frames representingseveral seconds or minutes of video), and send the video to an actionclassifier 1403, which classifies the activities in the video beforesending them an known activities to the action detector 1402.

The action classifier 1403 comprises one or more machine learningalgorithms that have been trained to classify human actions.Classification of human action is a simpler activity than human actiondetection. Human action classification involves identification of humanobjects in the video and some classification of the activity beingdemonstrated by the human objects (e.g., standing, walking, running,jumping, etc.). Classification does not require a determination of whenthe action starts, where in the frame the action occurs, or the relativemotion of the action, it simply requires that an object in the video berecognized as a person and that the activity of that person beidentified.

The action detector 1402 received videos of known sexual activity (i.e.,those that have already been classified either manually or using machinelearning algorithms), and detects when the action starts, where in theframe the action occurs, or the relative motion of the action. Becausethe activity in the video is already known, machine learning algorithmsmay be employed which have been specially-trained for the type ofactivity depicted in the video. Action detection involves firstsegmenting the video into objects and backgrounds, identifying humanobjects in each frame of video, and tracking the movement of those humanobjects across video frames.

Both action classification and action detection rely on color-basedprocessing of pixels in each frame of the video. Most videos currentlyavailable, whether or not depicting sexual activity, are two-dimensional(2D) videos containing color information only (e.g., the RGB colormodel), from which depth information must be inferred. The additional ofdepth sensors allows the addition of depth information to the video data(e.g., RGBD color/depth model), which improves human pose estimation butrequires specialized sensors that must be used at the time of filming.Due to the processing-intensive nature of analyzing videos using machinelearning algorithms, some simplification techniques may be used toreduce the computing power required and/or speed up the processing time.For example, facial recognition algorithms have become widely used,fairly accurate, and can be implemented on computing devices with modestprocessing power. Thus, for videos where fellatio is known to be theprimary sexual activity, facial recognition algorithms may be used asthe machine learning component to track the relative position andorientation of the face in the video to indicate the movement componentof sexual activity. This greatly reduces the amount of computing powerrequired relative to videos containing unknown sexual activity and/orwhere whole body human activity must be classified and detected. Asthere is a limited range of possible sexual activity, and certain sexualactivities are more common than others, specially-trained machinelearning algorithms can be employed for given types of sexual activityto improve action classification and action detection times andaccuracy.

For both action classification and action detection, a variety ofmachine learning algorithms may be used. For example, as noted above, aconvolutional neural network (CNN) may be applied to performsegmentation of each video frame. Other machine learning algorithms orcombinations of machine learning algorithms may be employed. Forexample, a CNN may be employed to extract the features in the video,followed by a long short-term memory (LSTM) algorithm to evaluate thetemporal relationships between features. In another example, athree-dimensional CNN (3D CNN) may be employed which can directly createhierarchical representations of spatial and temporal relationships, thusobviating the need to processing through an LSTM. In another example, atwo-stream CNN may be used, wherein the first stream of input into theCNN is a set of temporal relationships that are established by apre-determined set of features, and the second stream is frames from thevideo. Action classification and/or action detection can be performed byaveraging the predictions of the CNN, or by using the output of the CNNfor each frame of the video as input to a 3D CNN. Many other variationsare possible, and while CNNs are particularly suitable for videoprocessing, other types of machine learning algorithms may be employed.

The clip annotator 1404 associates each video clip with action detectiondata synchronized with the playback times (or frames) of the video clip,and the clip re-integrator 1405 combines the clips back into theoriginal video received by the clip parser 1401. The annotated video, orjust the annotations data from the video, may then be used to generatedevice control data or may be further processed to extract models oftypical sexual activity prior to generating device control data.

Detailed Description of Exemplary Aspects

FIG. 1 shows the internal workings of an exemplary sexual stimulationdevice 100. The compatible device is a small handheld unit powered by alow voltage, external direct current (DC) power source. Inside thedevice is a metal framework 101 to which the mechanical parts of thedevice are attached. Attached to the metal framework 101 is a small DCmotor 102 with a motor shaft 103, which drives the stimulationmechanism. A screw shaft 104 is affixed to the motor shaft 103 of the DCmotor 102, such that the screw shaft 104 rotates as the motor shaft 103of the DC motor 102 rotates. The polarity of voltage to the DC motor 102may be reversed so that the motor shaft 103 of the DC motor 102 rotatesboth clockwise and counter-clockwise. A flex coupling 105 between themotor shaft 103 of the DC motor 102 and screw shaft 104 compensates forany misalignment between the two during operation. A screw collar 106 isplaced around the screw shaft 104 and attached to a bracket 107, whichis held in a particular orientation by guide rods 108, such that thescrew collar 106 and bracket 107 travel in a linear motion as the screwshaft, 104 is turned. Affixed to the bracket 107 is a gripper 109, whichtravels in a linear motion along with the bracket 107. A hole 110 in themetal framework 101, allows for the insertion of a flexible sleeve asshown in FIG. 2.

FIG. 2 shows additional components of the internal workings of anexemplary sexual stimulation device 200. A flexible sleeve 201 made ofeither thermoplastic elastomer (TPE) or thermoplastic rubber (TPR) isinserted through a large hole 109 in the metal framework 101 and throughgripper 108. Sleeve 201 is prevented from accidentally slipping intodevice 200 by a ridge 202 at the open end of sleeve 201, and is held inthe proper position by ridges 203 at both ends of gripper 108. Duringoperation, gripper 108 slides in a reciprocal linear motion 201providing pressure and motion against the penis inside the sleeve 201 ina manner similar to sexual intercourse or manual masturbation. Dependingon the configuration, gripper 108 may either grip sleeve 201 and movesleeve 201 along the penis, or it may slide along the outside of sleeve201, not moving the sleeve relative to the penis. Also depending onconfiguration, gripper 108 may be made of rigid, semi-rigid, orcompliant materials, and other shapes might be used (e.g., partial tube,ring, half-ring, multiple rings, loops of wire) and may contain rollersor bearings to increase, stimulation and reduce friction against theflexible sleeve 201.

FIG. 3 shows the external structure 300 of an exemplary sexualstimulation device. The housing 301 of the device is made of plastic,and is attached to the metal framework in such a way as to provideadditional support and structure to the device. User controls 302 in theform of buttons and switches and their associated electronics are builtinto the housing. The housing has an opening at one end corresponding tothe opening 109 in the metal framework 101, into which the flexiblesleeve 201 is inserted. The penis is inserted into the sleeve 201 at theend of the device, and is stimulated by the reciprocal linear motion ofthe gripper 108 inside the device. The user controls the speed, pattern,and location of stimulation using the controls 302 on the outside of thehousing 301.

FIG. 4 shows exemplary variations 400 of the sleeve 201 and gripper 108aspects of an exemplary sexual stimulation device. As noted above,different configurations of the sleeve 201 and gripper 108 are possibleto allow optimal fit and sensation for penises of different lengths andgirths, and to allow the user a choice of pressure, gripper location,and sensation. Sleeve variant one 401 has a thin top wall 402 with a lowpoint of attachment 403 to the gripper 108. Sleeve variant two 404 has athin top wall 405 with a middle point of attachment 406 to the gripper108. Sleeve variant three 407 has a uniform wall thickness 408 with amiddle point of attachment 409 to the gripper 108. Sleeve variant four410 has a bellows top 411, a thin wall 412, and a middle point ofattachment 413. Sleeve variant five 414 has an extended bellows 415 andno attachment to the gripper 108 other than a stopper at the end 416,allowing the gripper 108 to slide along the outside of the sleeve 414.Sleeve variant six 417 has a uniform wall thickness 418 and noattachment to the gripper 108 other than a stopper at the end 419,allowing the gripper 108 to slide along the outside of the sleeve 417.Sleeve variant seven 420 has a full bellows design 421 and no attachmentto the gripper 108 other than a stopper at the end 422, allowing thegripper 108 to slide along the outside of the sleeve 420. Sleeve varianteight 423 has a full bellows design with large grooves 424 into whichfits a gripper made of wire loops with beads attached 425.

FIG. 5 shows the internal workings of an exemplary sexual stimulationdevice 500. The compatible device is a small handheld unit powered by alow voltage, external direct current (DC) power source. Inside thedevice is a metal framework 501 to which the mechanical parts of thedevice are attached. Attached to the metal framework 501 is a small DCmotor 502 with a motor shaft 503, which drives the stimulationmechanism. A screw shaft 504 is affixed directly to the motor shaft 503of the DC motor 502, such that the screw shaft 504 rotates as the motorshaft 503 of the DC motor 502 rotates. The polarity of voltage to the DCmotor 502 may be reversed so that the motor shaft 503 of the DC motor502 rotates both clockwise and counter-clockwise. In this embodiment,the flex coupling 105 has been eliminated, allowing the device to beconstructed in a more compact form, approximately 2 cm shorter inoverall length. A screw collar 505 is placed around the screw shaft 504and attached to a bracket 506, which is held in a particular orientationby guide rods 507 such that the screw collar 505 and bracket 506 travelin a linear motion as the screw shaft 504 is turned. Affixed to thebracket 506 is a gripper 508, which travels in a linear motion alongwith the bracket 506. A hole 509 in the metal framework 501 allows forthe insertion of a flexible sleeve 201 as previously shown in FIG. 2.FIG. 6 shows additional exemplary variations 600 of the sleeve aspect ofan exemplary sexual stimulation device as set forth in another preferredembodiment. In this embodiment, the opening in the sleeve may be otherthan circular. For example, the opening may be elliptical in shape 601or triangular in shape 602.

FIG. 6 shows additional exemplary variations of the aspects of anexemplary sexual stimulation device.

FIG. 15 (PRIOR ART) is a diagram describing the use of the local binarypattern (LBP) algorithm to extract the textural structure of an imagefor use in object detection. There are a wide variety of algorithms forextracting data from images and/or video (which is a series of images)for object recognition within the image. The local binary pattern (LBP)algorithm is one of the simplest and easiest to understand, and istherefore used here to demonstrate in general terms how image data isprocessed to extract certain information. All digital images arecomposed of pixels, each of which represents the smallest area ofviewable information in the image (i.e., each pixel is a “dot” in theimage). Each pixel contains information about the color that the dotrepresents, and the color of the pixel may be either black and white,grayscale, or colored. The representation of the color may be in anynumber of standard formats (also called color models), with thehexadecimal (HEX), red, green, blue (RGB), and cyan, magenta, yellow,key/black (CMYK) being three of the most common. In this simplifiedexample, the original image 1501 is in 256-bit grayscale, meaning thateach pixel in the original image 1501 has a grayscale value of 0-255.The LBP algorithm is applied to each pixel in the original image 1501 byselecting a pixel and comparing the value of that pixel to the value ofeach surrounding pixel, as shown in the first table of values 1502, inwhich the selected pixel from the original image 1501 has a value of 90,and the values of the surrounding pixels from top left and goingclockwise are 30, 50, 70, 120, 220, 180, 80, and 20. In a next step ofthe LBP algorithm, for each of the pixels in the first table 1502 isassigned a binary (zero or one) value in a second table 1503, wherein azero is assigned if the value of the pixel is less than the value of theselected (i.e., center) pixel, and a one is assigned if the value of thepixel is equal to or greater than the value of the selected (i.e.,center) pixel. The resulting values are shown in the second table 1503,wherein the pixels with values of 90, 120, 220, and 180 have beenassigned a binary value of one, and all of the other pixels have beenassigned a value of zero. The values of each of the pixels in the secondtable 1503 surrounding the selected (i.e., center) pixel areconcatenated together in a clockwise manner starting from the top left,resulting in this case in the binary number 00011100. This binary numberis then converted back to a decimal number, in this case 28, and thisdecimal number is substituted in for the value of the selected pixel inthe original image 1501, representing a 256-bit grayscale value for thelocal area in which the selected pixel resides. This process is repeatedfor all pixels in the original image 1501, resulting in a texturizedimage 1504 wherein each pixel represents the “texture” of thesurrounding pixels from the original image 1501. Many differentprocessing methods can be used on the texturized image to identifyfeatures and Objects in the texturized image, such division of the imageinto blocks and extracting histograms of each block, and running thehistograms through machine learning algorithms that have been trained toidentify features from similar histograms from similar images.

FIG. 16 (PRIOR ART) is a diagram describing the use of a convolutionalneural network (CNN) to identify objects in an image by segmenting theobjects from the background of the image. Artificial neural networks arecomputing systems that mimic the function of the biological neuralnetworks that constitute human and animal brains. Artificial neuralnetworks comprise a series of “nodes” which loosely model the neurons inthe brain. Each node can pass on a signal to other nodes. The output ofeach node is some non-linear function of the sum of its inputs, and theprobability of a signal being passed to another node depends on theweight assigned to the “edge” between the nodes, which is the connectionbetween the nodes. An artificial neural network finds the correctmathematical relationship between an input and an output by calculatinga probability of obtaining the output from the input at each “layer” ofmathematical calculations.

Convolutional neural networks are a type of artificial neural networkcommonly used to analyze imagery that use a mathematical operationcalled convolution (also called a dot product or cross-correlation)instead of general matrix multiplication as in other types of artificialneural networks. Convolutional neural networks are fully connected,meaning that each node in one layer is connected to every node in thenext layer. Each layer of the CNN convolves the input from the previouslayer. Each convolutional node processes data only for its receptiveheld, which is typically a small sub-area of the image (e.g., a 5×5square of pixels). There may be pooling layers in a CNN which reduce thedimensionality of the data by combining the outputs of node clusters inone layer into a single node in the next layer. Each node in a CNNcomputes an output value by applying a specific function to the inputvalues coming from the receptive field in the previous layer. Thefunction that is applied to the input values is determined by a vectorof weights and a bias. The CNN “learns” by making iterative: adjustmentsto these biases and weights.

In this application of CNNs, an input image 1601 is processed through aCNN in which there are two stages, a convolution stage 1602 and ade-convolution stage 1603, ultimately resulting in an output image 1604in which objects in the image are segmented (i.e., identified asseparate from) the background of the image. In the convolution stage1602, the image is processed through multiple convolution layers toextract features from the image, and then through a pooling layer toreduce the dimensionality of the data (i.e., aggregation of pixels) forthe next round of convolutions. After several rounds of convolution andpooling, the features have been extracted and the data have been reducedto a manageable size. The data are then passed to the de-convolutionstage 1603, in which a prediction is made as to whether each pixel orgroup of pixels represents an object, and passed through several layersof de-convolution before a new prediction is made at a larger level ofde-aggregation of the pixels. This process repeats until an output image1604 is obtained of a similar size as the input image 1601, wherein eachpixel of the output image 1604 is labeled with an indication as towhether it represents an object or background.

FIG. 17 is a diagram showing exemplary video annotation data collectionand processing to develop models of sexual activity sequences. In afirst step, annotation data from videos depicting sexual activity isgathered. The diagram at 1710 shows an exemplary graph created fromannotation data from a single video depicting sexual activity. The graphof the annotation data shows the relative position of an object in asingle video over time (i.e., movement of the object over time in thatvideo). A number of patterns of movement 1711-1715 can be seen in thegraph. When used in conjunction with a single video, the annotation datacan be converted directly into device control data for a sexualstimulation device, and the device can be used in synchronization withthe video just from the annotation data for that video. However, ifmodels of sexual activity are to be created for use with the sexualstimulation device (e.g., to mimic “typical” sexual activities butwithout reference to a particular video additional processing isrequired to develop models from the annotated data.

To process annotation data to develop models, patterns of movement willideally be extracted from a larger number of videos. When a machinelearning algorithm is fed the annotation data from many such videos,these patterns can be identified across the various videos, and thefrequency of these patterns across all videos can be extracted, as shownin the bar chart at 1720. In this bar chart 1720, one hundred totalhours of video time was processed through the machine learningalgorithm, and the number of hours each pattern of movement 1711-1715was displayed is shown. For example, Pattern 4 was displayed in a totalof 40 hours out of the 100 total hours of video. Machine learningalgorithms suitable for this identification of patterns across videosare clustering-type algorithms such as K-means clustering (also known asLloyd's algorithm), in which movement patterns in the annotation dataare clustered into groups containing similar movement patterns. From theclusters, certain types of movement patterns can be identified. Forexample, in the case of a video depicting fellatio, clusters of movementwill show shallow motions around the tip of the penis (e.g., Pattern 41714), deep motions around the base of the penis (e.g., Pattern 1),movements along the full length of the penis (e.g., Pattern 3), etc.Such clusters may be visually mapped in 2D or 3D to confirm theconsistency and accuracy of the clustering.

Finally, other types of machine learning algorithms may be employed tocreate models of sexual activity shown in the processed annotation data.In one method, reinforcement learning may be employed to identify thefrequency counts of certain patterns of movement, create “states”representing these patterns, and probabilities of transferring from anygiven state to any other state. An example of such a state diagram isshown at 1730, wherein each state represents one of the patterns ofmovement 1711-1715, and the lines and percentages indicate theprobability of transitioning to a different state. In the diagram at1730, Pattern 5 1715 is shown as the current state, and probabilities ofall possible transitions to and from the current state are shown. Inpractice, this state diagram 1730 would be expanded to include theprobabilities to and from each state to every other state, but thisdiagram is simplified to show only transitions to and from the currentstate. From these state transition probabilities, sequences of movementpatterns 1711-1715 may be constructed representing models of the“typical” activities shown in the video. If annotation data areprocessed for selected types of videos (e.g., videos containing certaintypes of sexual activity, certain actors or actresses, or videos from acertain film studio or director, etc.), the models will berepresentative of that selected type of video. Alternatively, a widevariety of deep learning algorithms may be used for this processincluding, but not limited to, dense neural networks, convolutionalneural networks, generative adversarial networks, and recurrent neuralnetworks. Each of these types of machine learning algorithms may beemployed to identify sequences of the patterns of movement identified inthe clustering at the previous stage.

FIG. 18 is a flow diagram showing a method for developing models ofsexual activity sequences from selected videos. In a first step,annotation data are received for a plurality of videos of a particulartype (e.g., videos containing certain types of sexual activity, certainactors or actresses, or videos from a certain film studio or director,etc.) 1801. Next, the annotation data are processed machine learningalgorithms to detect and classify patterns of movement 1802. Then, thedetected patterns of movement are further processed through machinelearning algorithms to identify sequences of patterns of movement thatare common for that selected type of video 1803, which are then turnedinto models representative of the types of sexual activity depicted.Optionally, the patterns and sequences of movement may be classifiedbased on metadata associated with the video or based on human input1804. For example, a particular sequence may be classified as a typicalrepresentation of fellatio by a particular adult film star from acertain decade. Lastly, after the models are created, device controlmodes or functions based on the models may be created 1805 and storedfor later use or programmed into the sexual stimulation device.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different, types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 19, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited rely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 19 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine- readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Peri, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 20, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 19). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 21, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 20. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises. In addition to local storage on servers 32, remotestorage 38 may be accessible through the network(s) 31.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 in either local or remote storage 38 may be used orreferred to by one or more aspects. It should be understood by onehaving ordinary skill in the art that databases in storage 34 may bearranged in a wide variety of architectures and using a wide variety ofdata access and manipulation means. For example, in various aspects oneor more databases in storage 34 may comprise a relational databasesystem using a structured query language (SQL), while others maycomprise an alternative data storage technology such as those referredto in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLEBIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 22 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via, a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for automated generation of controlsignals for sexual stimulation devices from videos of sexual activity,comprising: a video analysis engine comprising a first plurality ofprogramming instructions stored in the memory of, and operating on atleast one processor of, a computer system, wherein the first pluralityof programming instructions, when operating on the processor, causes thecomputer system to: receive a plurality of videos representing a type ofsexual activity; process each video through a first machine learningalgorithm trained to classify objects in the video; identify a patternof movement of an object in each classified video; process the patternof movement identified in each classified video through a second machinelearning algorithm trained to identify a plurality of pattern states andprobabilities of transferring from one of the plurality of patternstates to another of the plurality of pattern states; create a sequenceof pattern states from the pattern states and probabilities, thesequence representing a model of typical movements found in the type ofsexual activity represented by the plurality of videos; and based on themodel, generate one or more control signals for a sexual stimulationdevice, the control signals corresponding to the typical movements. 2.The system of claim 1, further comprising a biometric sensor receivercomprising a second plurality of programming instructions stored in thememory of, and operating on at least one processor of, the computersystem, wherein the second plurality of programming instructions, whenoperating on the processor, causes the computer system to: receivebiometric data of a user from one or more biometric sensors; and adjustthe one or more control signals based on the biometric data prior to orduring transmission to a sexual stimulation device.
 3. A method forautomated generation of control signals for sexual stimulation devicesfrom videos of sexual activity, comprising the steps of: receiving aplurality of videos representing a type of sexual activity; processingeach video through a first machine learning algorithm trained toclassify objects in the video; identifying a pattern of movement of anobject in each classified video; processing the pattern of movementidentified in each classified video through a second machine learningalgorithm trained to identify a plurality of pattern states andprobabilities of transferring from one of the plurality of patternstates to another of the plurality of pattern states; creating asequence of pattern states from the pattern states and probabilities,the sequence representing a model of typical movements found in the typeof sexual activity represented by the plurality of videos; andgenerating, based on the model, one or more control signals for a sexualstimulation device, the control signals corresponding to the typicalmovements.
 4. The method of claim 3, further comprising the steps of:receiving biometric data of a user from one or more biometric sensors;and adjusting the one or more control signals based on the biometricdata prior to or during transmission to a sexual stimulation device. 5.A method for automated generation of control signals for sexualstimulation devices from videos of sexual activity, comprising the stepsof: receiving a plurality of videos representing a type of sexualactivity; receive data comprising a pattern of movement of an object ineach video; processing the pattern of movement identified in each videothrough a machine learning algorithm trained to identify a plurality ofpattern states and probabilities of transferring from one of theplurality of pattern states to another of the plurality of patternstates; creating a sequence of pattern states from the pattern statesand probabilities, the sequence representing a model of typicalmovements found in the type of sexual activity represented by theplurality of videos; and generating, based on the model, one or morecontrol signals for a sexual stimulation device, the control signalscorresponding to the typical movements.
 6. The method of claim 5,further comprising the steps of: receiving biometric data of a user fromone or more biometric sensors; and adjusting the one or more controlsignals based on the biometric data prior to or during transmission to asexual stimulation device.